{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ggplot import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xVo/CdTI6OnpdX6+joyP9/f0ZGRmRs1nq7u6+7v+dbmYyNj9yNnsyNj8yNjcd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\n8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1099a5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (278504885)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(diamonds, aes(x='price')) + geom_histogram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fbdra2tLR0XENJloYJvI1Xc54h4zNjozNjZzNnIzNjYzNzkLK14yOZHx8PDt37szq1atz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\n99L2lMxp2dkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109991f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (278840261)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qplot(diamonds.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7W/zqV786c+ztt98efX198apXvSo+8IEPxDe/+c2IiLjnnnvi05/+dFSr1bjk\nkkvik5/8ZLseDkBHE6xAIb3whS888+f+/v5Vf5+ZmTnz90svvfTMn3ft2hXHjh2LiIhjx46t+DeX\nAwC0hmAFWMef//znM3+emJiISy65JCIiduzYEX/5y19W/BsAzSdYAdbxuc99Lubm5uJ3v/td7N+/\nP975zndGRMR//ud/xuc///l46qmn4oknnoivfvWrbZ4UoDMJVqBwnvmNUet9o9TY2Fi89KUvjRtv\nvDE+9alPxfXXXx8REZ/5zGdieHg4LrvssnjTm94Ut956a/T29rZsboCiKjUajUa7hwDI6PHHH48X\nv/jFUavVoqtr/a/v77zzzvj2t78d999//yZMB1Ac3mEFOIdzfU1//Pjx+MUvfhGNRiMeeuihuOOO\nO+KWW27ZxOkAisHPYQU4h3NdLrC4uBgf+chH4ujRozE4OBjvete74qMf/egmTgdQDC4JAAAgNZcE\nAACQmmAFACA1wQoAQGqCFQCA1AQrAACpCVYAAFL7f2uyHjeGFML0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1025d5b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (278873585)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(aes(x='mpg', weight='wt'), mtcars) + geom_histogram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
